01952nas a2200337   4500008004100000022001400041245006800055210006600123300001800189490000700207520101700214653002001231653001401251653000901265653002501274653000901299653001001308653001901318653000901337653001101346653001001357100002901367700002101396700002001417700001901437700002201456700002401478700001901502700002101521856007201542       2022                        eng d  a0960-982200aA neural population selective for song in human auditory cortex0 aneural population selective for song in human auditory cortex  a1470-1484.e120 v323 aSummary How is music represented in the brain? While neuroimaging has revealed some spatial segregation between responses to music versus other sounds, little is known about the neural code for music itself. To address this question, we developed a method to infer canonical response components of human auditory cortex using intracranial responses to natural sounds, and further used the superior coverage of fMRI to map their spatial distribution. The inferred components replicated many prior findings, including distinct neural selectivity for speech and music, but also revealed a novel component that responded nearly exclusively to music with singing. Song selectivity was not explainable by standard acoustic features, was located near speech- and music-selective responses, and was also evident in individual electrodes. These results suggest that representations of music are fractionated into subpopulations selective for different types of music, one of which is specialized for the analysis of song.10aAuditory Cortex10acomponent10aECoG10aElectrocorticography10afMRI10amusic10anatural sounds10asong10aSpeech10avoice1 aNorman-Haignere, Sam, V.1 aFeather, Jenelle1 aBoebinger, Dana1 aBrunner, Peter1 aRitaccio, Anthony1 aMcDermott, Josh, H.1 aSchalk, Gerwin1 aKanwisher, Nancy  uhttps://www.sciencedirect.com/science/article/pii/S096098222200131200831nas a2200277   4500008004100000022001400041245009500055210006900150300001600219490000800235653000900243653002500252653002300277653001700300653002300317100001600340700001500356700001400371700001900385700001400404700001400418700001600432700001900448700001500467856007100482       2018                        eng d  a1388-245700aPassive functional mapping of receptive language areas using electrocorticographic signals0 aPassive functional mapping of receptive language areas using ele  a2517 - 25240 v12910aECoG10aElectrocorticography10afunctional mapping10aIntracranial10aReceptive language1 aSwift, J.R.1 aCoon, W.G.1 aGuger, C.1 aBrunner, Peter1 aBunch, M.1 aLynch, T.1 aFrawley, B.1 aRitaccio, A.L.1 aSchalk, G.  uhttp://www.sciencedirect.com/science/article/pii/S138824571831228801929nas a2200265   4500008004100000245010300041210006900144260000800213300001100221490000600232520106300238653000801301653003001309653000901339653002501348653002501373100002001398700001901418700001801437700002401455700002601479700001901505700001901524856012001543       2016                        eng d00aDifferential roles of high gamma and local motor potentials for movement preparation and execution0 aDifferential roles of high gamma and local motor potentials for   cMay  a88-1020 v33 aDetermining a person’s intent, such as the planned direction of their movement, directly from their cortical activity could support important applications such as brain-computer interfaces (BCIs). Continuing development of improved BCI systems requires a better understanding of how the brain prepares for and executes movements. To contribute to this understanding, we recorded surface cortical potentials (electrocorticographic signals; ECoG) in 11 human subjects performing a delayed center-out task to establish the differential role of high gamma activity (HGA) and the local motor potential (LMP) as a function of time and anatomical area during movement preparation and execution. High gamma modulations mostly confirm previous findings of sensorimotor cortex involvement, whereas modulations in LMPs are observed in prefrontal cortices. These modulations include directional information during movement planning as well as execution. Our results suggest that sampling signals from these widely distributed cortical areas improves decoding accuracy.10aBCI10abrain-computer interfaces10aECoG10aElectrocorticography10asensorimotor systems1 aGunduz, Aysegul1 aBrunner, Peter1 aSharma, Mohit1 aLeuthardt, Eric, C.1 aRitaccio, Anthony, L.1 aPesaran, Bijan1 aSchalk, Gerwin  uhttps://www.neurotechcenter.org/publications/2016/differential-roles-high-gamma-and-local-motor-potentials-movement02272nas a2200277   4500008004100000245008600041210006900127260001200196520140600208653003301614653002901647653002001676653000901696653002501705653002401730653002001754653002201774100001401796700001401810700002401824700001501848700001901863700001901882700001601901856007701917       2015                        eng d00aBrain-to-text: Decoding spoken sentences from phone representations in the brain.0 aBraintotext Decoding spoken sentences from phone representations  c06/20153 aIt has long been speculated whether communication between humans and machines based on natural speech related cortical activity is possible. Over the past decade, studies have suggested that it is feasible to recognize isolated aspects of speech from neural signals, such as auditory features, phones or one of a few isolated words. However, until now it remained an unsolved challenge to decode continuously spoken speech from the neural substrate associated with speech and language processing. Here, we show for the first time that continuously spoken speech can be decoded into the expressed words from intracranial electrocorticographic (ECoG) recordings.Specifically, we implemented a system, which we call Brain-To-Text that models single phones, employs techniques from automatic speech recognition (ASR), and thereby transforms brain activity while speaking into the corresponding textual representation. Our results demonstrate that our system can achieve word error rates as low as 25% and phone error rates below 50%. Additionally, our approach contributes to the current understanding of the neural basis of continuous speech production by identifying those cortical regions that hold substantial information about individual phones. In conclusion, the Brain-To- Text system described in this paper represents an important step toward human-machine communication based on imagined speech.10aautomatic speech recognition10abrain-computer interface10abroadband gamma10aECoG10aElectrocorticography10apattern recognition10aspeech decoding10aspeech production1 aHerff, C.1 aHeger, D.1 ade Pesters, Adriana1 aTelaar, D.1 aBrunner, Peter1 aSchalk, Gerwin1 aSchultz, T.  uhttp://journal.frontiersin.org/article/10.3389/fnins.2015.00217/abstract02627nas a2200277   4500008004100000245009500041210006900136260001200205490000600217520181900223653002602042653001402068653000902082653000802091653002802099653000802127100002202135700002002157700001702177700001902194700001902213700002602232700002302258700002002281856004802301       2014                        eng d00aAssessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.0 aAssessing dynamics spatial scale and uncertainty in taskrelated   c03/20140 v83 aThe brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.10acanonical correlation10acoherence10aECoG10aEEG10afunctional connectivity10aMEG1 aStephen, Emily, P1 aLepage, Kyle, Q1 aEden, Uri, T1 aBrunner, Peter1 aSchalk, Gerwin1 aBrumberg, Jonathan, S1 aGuenther, Frank, H1 aKramer, Mark, A  uhttp://www.ncbi.nlm.nih.gov/pubmed/2467829501958nas a2200253   4500008004100000245010000041210006900141260001200210490000600222520117500228653002901403653000901432653003401441653003001475653002401505653002901529100001201558700002001570700001901590700001801609700001001627700001901637856004801656       2012                        eng d00aDecoding Onset and Direction of Movements using Electrocorticographic (ECoG) Signals in Humans.0 aDecoding Onset and Direction of Movements using Electrocorticogr  c08/20120 v53 aCommunication of intent usually requires motor function. This requirement can be limiting when a person is engaged in a task, or prohibitive for some people suffering from neuromuscular disorders. Determining a person's intent, e.g., where and when to move, from brain signals rather than from muscles would have important applications in clinical or other domains. For example, detection of the onset and direction of intended movements may provide the basis for restoration of simple grasping function in people with chronic stroke, or could be used to optimize a user's interaction with the surrounding environment. Detecting the onset and direction of actual movements are a first step in this direction. In this study, we demonstrate that we can detect the onset of intended movements and their direction using electrocorticographic (ECoG) signals recorded from the surface of the cortex in humans. We also demonstrate in a simulation that the information encoded in ECoG about these movements may improve performance in a targeting task. In summary, the results in this paper suggest that detection of intended movement is possible, and may serve useful functions.10abrain computer interface10aECoG10amovement direction prediction10amovement onset prediction10aneurorehabilitation10aperformance augmentation1 aWang, Z1 aGunduz, Aysegul1 aBrunner, Peter1 aRitaccio, A L1 aJi, Q1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/22891058